Overview

Dataset statistics

Number of variables40
Number of observations1000
Missing cells1000
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory312.6 KiB
Average record size in memory320.1 B

Variable types

Numeric14
Categorical24
Boolean1
Unsupported1

Alerts

policy_bind_date has a high cardinality: 951 distinct values High cardinality
incident_date has a high cardinality: 60 distinct values High cardinality
incident_location has a high cardinality: 1000 distinct values High cardinality
months_as_customer is highly correlated with ageHigh correlation
age is highly correlated with months_as_customerHigh correlation
total_claim_amount is highly correlated with injury_claim and 2 other fieldsHigh correlation
injury_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
property_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
vehicle_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
months_as_customer is highly correlated with ageHigh correlation
age is highly correlated with months_as_customerHigh correlation
total_claim_amount is highly correlated with injury_claim and 2 other fieldsHigh correlation
injury_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
property_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
vehicle_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
months_as_customer is highly correlated with ageHigh correlation
age is highly correlated with months_as_customerHigh correlation
total_claim_amount is highly correlated with injury_claim and 2 other fieldsHigh correlation
injury_claim is highly correlated with total_claim_amount and 1 other fieldsHigh correlation
property_claim is highly correlated with total_claim_amount and 1 other fieldsHigh correlation
vehicle_claim is highly correlated with total_claim_amount and 2 other fieldsHigh correlation
incident_type is highly correlated with collision_type and 1 other fieldsHigh correlation
incident_severity is highly correlated with fraud_reportedHigh correlation
auto_make is highly correlated with auto_modelHigh correlation
collision_type is highly correlated with incident_typeHigh correlation
number_of_vehicles_involved is highly correlated with incident_typeHigh correlation
fraud_reported is highly correlated with incident_severityHigh correlation
auto_model is highly correlated with auto_makeHigh correlation
months_as_customer is highly correlated with ageHigh correlation
age is highly correlated with months_as_customerHigh correlation
incident_type is highly correlated with collision_type and 7 other fieldsHigh correlation
collision_type is highly correlated with incident_type and 7 other fieldsHigh correlation
incident_severity is highly correlated with incident_type and 6 other fieldsHigh correlation
authorities_contacted is highly correlated with incident_type and 5 other fieldsHigh correlation
number_of_vehicles_involved is highly correlated with incident_type and 1 other fieldsHigh correlation
total_claim_amount is highly correlated with incident_type and 6 other fieldsHigh correlation
injury_claim is highly correlated with incident_type and 6 other fieldsHigh correlation
property_claim is highly correlated with incident_type and 6 other fieldsHigh correlation
vehicle_claim is highly correlated with incident_type and 6 other fieldsHigh correlation
auto_make is highly correlated with auto_modelHigh correlation
auto_model is highly correlated with auto_makeHigh correlation
fraud_reported is highly correlated with incident_severityHigh correlation
_c39 has 1000 (100.0%) missing values Missing
policy_bind_date is uniformly distributed Uniform
incident_location is uniformly distributed Uniform
policy_number has unique values Unique
incident_location has unique values Unique
_c39 is an unsupported type, check if it needs cleaning or further analysis Unsupported
umbrella_limit has 798 (79.8%) zeros Zeros
capital-gains has 508 (50.8%) zeros Zeros
capital-loss has 475 (47.5%) zeros Zeros
incident_hour_of_the_day has 52 (5.2%) zeros Zeros
injury_claim has 25 (2.5%) zeros Zeros
property_claim has 19 (1.9%) zeros Zeros

Reproduction

Analysis started2022-06-26 17:03:23.170720
Analysis finished2022-06-26 17:05:09.493997
Duration1 minute and 46.32 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

months_as_customer
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct391
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.954
Minimum0
Maximum479
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:09.757848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.9
Q1115.75
median199.5
Q3276.25
95-th percentile429.05
Maximum479
Range479
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation115.1131744
Coefficient of variation (CV)0.5644075352
Kurtosis-0.4854280674
Mean203.954
Median Absolute Deviation (MAD)80.5
Skewness0.3621768478
Sum203954
Variance13251.04293
MonotonicityNot monotonic
2022-06-26T10:05:10.101644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1948
 
0.8%
1287
 
0.7%
2547
 
0.7%
1407
 
0.7%
2107
 
0.7%
2307
 
0.7%
2857
 
0.7%
1017
 
0.7%
2396
 
0.6%
1266
 
0.6%
Other values (381)931
93.1%
ValueCountFrequency (%)
01
 
0.1%
13
0.3%
22
0.2%
32
0.2%
43
0.3%
52
0.2%
61
 
0.1%
71
 
0.1%
83
0.3%
92
0.2%
ValueCountFrequency (%)
4792
0.2%
4782
0.2%
4761
0.1%
4752
0.2%
4731
0.1%
4721
0.1%
4681
0.1%
4671
0.1%
4651
0.1%
4641
0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.948
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:10.461094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q132
median38
Q344
95-th percentile57
Maximum64
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.140286694
Coefficient of variation (CV)0.2346792311
Kurtosis-0.260255015
Mean38.948
Median Absolute Deviation (MAD)6
Skewness0.4789880471
Sum38948
Variance83.54484084
MonotonicityNot monotonic
2022-06-26T10:05:10.789269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
4349
 
4.9%
3948
 
4.8%
4145
 
4.5%
3444
 
4.4%
3842
 
4.2%
3042
 
4.2%
3142
 
4.2%
3741
 
4.1%
3339
 
3.9%
4038
 
3.8%
Other values (36)570
57.0%
ValueCountFrequency (%)
191
 
0.1%
201
 
0.1%
216
 
0.6%
221
 
0.1%
237
 
0.7%
2410
 
1.0%
2514
1.4%
2626
2.6%
2724
2.4%
2830
3.0%
ValueCountFrequency (%)
642
 
0.2%
632
 
0.2%
624
 
0.4%
6110
1.0%
609
0.9%
595
 
0.5%
588
0.8%
5716
1.6%
568
0.8%
5514
1.4%

policy_number
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546238.648
Minimum100804
Maximum999435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:11.570647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100804
5-th percentile143969.6
Q1335980.25
median533135
Q3759099.75
95-th percentile954279.1
Maximum999435
Range898631
Interquartile range (IQR)423119.5

Descriptive statistics

Standard deviation257063.0053
Coefficient of variation (CV)0.4706056706
Kurtosis-1.132637689
Mean546238.648
Median Absolute Deviation (MAD)210974
Skewness0.03899064218
Sum546238648
Variance6.608138868 × 1010
MonotonicityNot monotonic
2022-06-26T10:05:11.914455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5215851
 
0.1%
6877551
 
0.1%
6744851
 
0.1%
2234041
 
0.1%
9914801
 
0.1%
8042191
 
0.1%
4830881
 
0.1%
1008041
 
0.1%
9418071
 
0.1%
5934661
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
1008041
0.1%
1014211
0.1%
1045941
0.1%
1061861
0.1%
1068731
0.1%
1071811
0.1%
1082701
0.1%
1088441
0.1%
1093921
0.1%
1100841
0.1%
ValueCountFrequency (%)
9994351
0.1%
9988651
0.1%
9981921
0.1%
9968501
0.1%
9962531
0.1%
9945381
0.1%
9938401
0.1%
9921451
0.1%
9915531
0.1%
9914801
0.1%

policy_bind_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct951
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2006-01-01
 
3
1992-04-28
 
3
1992-08-05
 
3
1991-12-14
 
2
2004-08-09
 
2
Other values (946)
987 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique905 ?
Unique (%)90.5%

Sample

1st row2014-10-17
2nd row2006-06-27
3rd row2000-09-06
4th row1990-05-25
5th row2014-06-06

Common Values

ValueCountFrequency (%)
2006-01-013
 
0.3%
1992-04-283
 
0.3%
1992-08-053
 
0.3%
1991-12-142
 
0.2%
2004-08-092
 
0.2%
2010-01-282
 
0.2%
1999-09-292
 
0.2%
2001-09-252
 
0.2%
2000-05-042
 
0.2%
1997-02-032
 
0.2%
Other values (941)977
97.7%

Length

2022-06-26T10:05:12.211365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2006-01-013
 
0.3%
1992-08-053
 
0.3%
1992-04-283
 
0.3%
1996-09-212
 
0.2%
2014-07-052
 
0.2%
1997-05-152
 
0.2%
1992-04-142
 
0.2%
2003-03-092
 
0.2%
1997-11-072
 
0.2%
1992-01-052
 
0.2%
Other values (941)977
97.7%

Most occurring characters

ValueCountFrequency (%)
02315
23.2%
-2000
20.0%
11609
16.1%
21298
13.0%
91105
11.1%
3308
 
3.1%
4299
 
3.0%
7275
 
2.8%
8274
 
2.7%
5263
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8000
80.0%
Dash Punctuation2000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02315
28.9%
11609
20.1%
21298
16.2%
91105
13.8%
3308
 
3.9%
4299
 
3.7%
7275
 
3.4%
8274
 
3.4%
5263
 
3.3%
6254
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
-2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02315
23.2%
-2000
20.0%
11609
16.1%
21298
13.0%
91105
11.1%
3308
 
3.1%
4299
 
3.0%
7275
 
2.8%
8274
 
2.7%
5263
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02315
23.2%
-2000
20.0%
11609
16.1%
21298
13.0%
91105
11.1%
3308
 
3.1%
4299
 
3.0%
7275
 
2.8%
8274
 
2.7%
5263
 
2.6%

policy_state
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
OH
352 
IL
338 
IN
310 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowIN
3rd rowOH
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
OH352
35.2%
IL338
33.8%
IN310
31.0%

Length

2022-06-26T10:05:12.445798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:12.727085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
oh352
35.2%
il338
33.8%
in310
31.0%

Most occurring characters

ValueCountFrequency (%)
I648
32.4%
O352
17.6%
H352
17.6%
L338
16.9%
N310
15.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I648
32.4%
O352
17.6%
H352
17.6%
L338
16.9%
N310
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I648
32.4%
O352
17.6%
H352
17.6%
L338
16.9%
N310
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I648
32.4%
O352
17.6%
H352
17.6%
L338
16.9%
N310
15.5%

policy_csl
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
250/500
351 
100/300
349 
500/1000
300 

Length

Max length8
Median length7
Mean length7.3
Min length7

Characters and Unicode

Total characters7300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250/500
2nd row250/500
3rd row100/300
4th row250/500
5th row500/1000

Common Values

ValueCountFrequency (%)
250/500351
35.1%
100/300349
34.9%
500/1000300
30.0%

Length

2022-06-26T10:05:12.914623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:13.133408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
250/500351
35.1%
100/300349
34.9%
500/1000300
30.0%

Most occurring characters

ValueCountFrequency (%)
03949
54.1%
51002
 
13.7%
/1000
 
13.7%
1649
 
8.9%
2351
 
4.8%
3349
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6300
86.3%
Other Punctuation1000
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03949
62.7%
51002
 
15.9%
1649
 
10.3%
2351
 
5.6%
3349
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03949
54.1%
51002
 
13.7%
/1000
 
13.7%
1649
 
8.9%
2351
 
4.8%
3349
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII7300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03949
54.1%
51002
 
13.7%
/1000
 
13.7%
1649
 
8.9%
2351
 
4.8%
3349
 
4.8%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1000
351 
500
342 
2000
307 

Length

Max length4
Median length4
Mean length3.658
Min length3

Characters and Unicode

Total characters3658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row2000
3rd row2000
4th row2000
5th row1000

Common Values

ValueCountFrequency (%)
1000351
35.1%
500342
34.2%
2000307
30.7%

Length

2022-06-26T10:05:13.320938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:13.524099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1000351
35.1%
500342
34.2%
2000307
30.7%

Most occurring characters

ValueCountFrequency (%)
02658
72.7%
1351
 
9.6%
5342
 
9.3%
2307
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02658
72.7%
1351
 
9.6%
5342
 
9.3%
2307
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common3658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02658
72.7%
1351
 
9.6%
5342
 
9.3%
2307
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02658
72.7%
1351
 
9.6%
5342
 
9.3%
2307
 
8.4%

policy_annual_premium
Real number (ℝ≥0)

Distinct991
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.40615
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:13.778701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile855.112
Q11089.6075
median1257.2
Q31415.695
95-th percentile1653.4435
Maximum2047.59
Range1614.26
Interquartile range (IQR)326.0875

Descriptive statistics

Standard deviation244.167395
Coefficient of variation (CV)0.1943379495
Kurtosis0.07388944021
Mean1256.40615
Median Absolute Deviation (MAD)164.26
Skewness0.004401994527
Sum1256406.15
Variance59617.71676
MonotonicityNot monotonic
2022-06-26T10:05:14.074953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1558.292
 
0.2%
1215.362
 
0.2%
1362.872
 
0.2%
1073.832
 
0.2%
1389.132
 
0.2%
1074.072
 
0.2%
1374.222
 
0.2%
1524.452
 
0.2%
1281.252
 
0.2%
1230.691
 
0.1%
Other values (981)981
98.1%
ValueCountFrequency (%)
433.331
0.1%
484.671
0.1%
538.171
0.1%
566.111
0.1%
617.111
0.1%
625.081
0.1%
653.661
0.1%
664.861
0.1%
671.011
0.1%
671.921
0.1%
ValueCountFrequency (%)
2047.591
0.1%
1969.631
0.1%
1935.851
0.1%
1927.871
0.1%
1922.841
0.1%
1896.911
0.1%
1878.441
0.1%
1865.831
0.1%
1863.041
0.1%
1861.431
0.1%

umbrella_limit
Real number (ℝ)

ZEROS

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101000
Minimum-1000000
Maximum10000000
Zeros798
Zeros (%)79.8%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2022-06-26T10:05:14.377447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range11000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2297406.598
Coefficient of variation (CV)2.086654494
Kurtosis1.79207731
Mean1101000
Median Absolute Deviation (MAD)0
Skewness1.806712199
Sum1101000000
Variance5.278077077 × 1012
MonotonicityNot monotonic
2022-06-26T10:05:14.549349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0798
79.8%
600000057
 
5.7%
500000046
 
4.6%
400000039
 
3.9%
700000029
 
2.9%
300000012
 
1.2%
80000008
 
0.8%
90000005
 
0.5%
20000003
 
0.3%
100000002
 
0.2%
ValueCountFrequency (%)
-10000001
 
0.1%
0798
79.8%
20000003
 
0.3%
300000012
 
1.2%
400000039
 
3.9%
500000046
 
4.6%
600000057
 
5.7%
700000029
 
2.9%
80000008
 
0.8%
90000005
 
0.5%
ValueCountFrequency (%)
100000002
 
0.2%
90000005
 
0.5%
80000008
 
0.8%
700000029
 
2.9%
600000057
 
5.7%
500000046
 
4.6%
400000039
 
3.9%
300000012
 
1.2%
20000003
 
0.3%
0798
79.8%

insured_zip
Real number (ℝ≥0)

Distinct995
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501214.488
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:14.783759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433273.75
Q1448404.5
median466445.5
Q3603251
95-th percentile617463.35
Maximum620962
Range190858
Interquartile range (IQR)154846.5

Descriptive statistics

Standard deviation71701.61094
Coefficient of variation (CV)0.1430557429
Kurtosis-1.190711054
Mean501214.488
Median Absolute Deviation (MAD)21841
Skewness0.8165539259
Sum501214488
Variance5141121012
MonotonicityNot monotonic
2022-06-26T10:05:15.109115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4776952
 
0.2%
4694292
 
0.2%
4468952
 
0.2%
4312022
 
0.2%
4566022
 
0.2%
4661321
 
0.1%
4522181
 
0.1%
6089821
 
0.1%
4596301
 
0.1%
4531931
 
0.1%
Other values (985)985
98.5%
ValueCountFrequency (%)
4301041
0.1%
4301411
0.1%
4302321
0.1%
4303801
0.1%
4305671
0.1%
4306211
0.1%
4306321
0.1%
4306651
0.1%
4307141
0.1%
4308321
0.1%
ValueCountFrequency (%)
6209621
0.1%
6208691
0.1%
6208191
0.1%
6207571
0.1%
6207371
0.1%
6205071
0.1%
6204931
0.1%
6204731
0.1%
6203581
0.1%
6202071
0.1%

insured_sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
FEMALE
537 
MALE
463 

Length

Max length6
Median length6
Mean length5.074
Min length4

Characters and Unicode

Total characters5074
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
FEMALE537
53.7%
MALE463
46.3%

Length

2022-06-26T10:05:15.390264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:15.658941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
female537
53.7%
male463
46.3%

Most occurring characters

ValueCountFrequency (%)
E1537
30.3%
M1000
19.7%
A1000
19.7%
L1000
19.7%
F537
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5074
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E1537
30.3%
M1000
19.7%
A1000
19.7%
L1000
19.7%
F537
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin5074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E1537
30.3%
M1000
19.7%
A1000
19.7%
L1000
19.7%
F537
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E1537
30.3%
M1000
19.7%
A1000
19.7%
L1000
19.7%
F537
 
10.6%
Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
JD
161 
High School
160 
Associate
145 
MD
144 
Masters
143 
Other values (2)
247 

Length

Max length11
Median length9
Mean length5.905
Min length2

Characters and Unicode

Total characters5905
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMD
2nd rowMD
3rd rowPhD
4th rowPhD
5th rowAssociate

Common Values

ValueCountFrequency (%)
JD161
16.1%
High School160
16.0%
Associate145
14.5%
MD144
14.4%
Masters143
14.3%
PhD125
12.5%
College122
12.2%

Length

2022-06-26T10:05:15.862105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:16.184416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
jd161
13.9%
high160
13.8%
school160
13.8%
associate145
12.5%
md144
12.4%
masters143
12.3%
phd125
10.8%
college122
10.5%

Most occurring characters

ValueCountFrequency (%)
o587
 
9.9%
s576
 
9.8%
e532
 
9.0%
h445
 
7.5%
D430
 
7.3%
l404
 
6.8%
i305
 
5.2%
c305
 
5.2%
t288
 
4.9%
a288
 
4.9%
Other values (10)1745
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4155
70.4%
Uppercase Letter1590
 
26.9%
Space Separator160
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o587
14.1%
s576
13.9%
e532
12.8%
h445
10.7%
l404
9.7%
i305
7.3%
c305
7.3%
t288
6.9%
a288
6.9%
g282
6.8%
Uppercase Letter
ValueCountFrequency (%)
D430
27.0%
M287
18.1%
J161
 
10.1%
S160
 
10.1%
H160
 
10.1%
A145
 
9.1%
P125
 
7.9%
C122
 
7.7%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5745
97.3%
Common160
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o587
 
10.2%
s576
 
10.0%
e532
 
9.3%
h445
 
7.7%
D430
 
7.5%
l404
 
7.0%
i305
 
5.3%
c305
 
5.3%
t288
 
5.0%
a288
 
5.0%
Other values (9)1585
27.6%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o587
 
9.9%
s576
 
9.8%
e532
 
9.0%
h445
 
7.5%
D430
 
7.3%
l404
 
6.8%
i305
 
5.2%
c305
 
5.2%
t288
 
4.9%
a288
 
4.9%
Other values (10)1745
29.6%
Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
machine-op-inspct
93 
prof-specialty
85 
tech-support
78 
sales
76 
exec-managerial
76 
Other values (9)
592 

Length

Max length17
Median length16
Mean length13.521
Min length5

Characters and Unicode

Total characters13521
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcraft-repair
2nd rowmachine-op-inspct
3rd rowsales
4th rowarmed-forces
5th rowsales

Common Values

ValueCountFrequency (%)
machine-op-inspct93
 
9.3%
prof-specialty85
 
8.5%
tech-support78
 
7.8%
sales76
 
7.6%
exec-managerial76
 
7.6%
craft-repair74
 
7.4%
transport-moving72
 
7.2%
other-service71
 
7.1%
priv-house-serv71
 
7.1%
armed-forces69
 
6.9%
Other values (4)235
23.5%

Length

2022-06-26T10:05:16.605729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct93
 
9.3%
prof-specialty85
 
8.5%
tech-support78
 
7.8%
sales76
 
7.6%
exec-managerial76
 
7.6%
craft-repair74
 
7.4%
transport-moving72
 
7.2%
other-service71
 
7.1%
priv-house-serv71
 
7.1%
armed-forces69
 
6.9%
Other values (4)235
23.5%

Most occurring characters

ValueCountFrequency (%)
e1543
11.4%
r1379
10.2%
-1088
 
8.0%
a1062
 
7.9%
s986
 
7.3%
i922
 
6.8%
c886
 
6.6%
p792
 
5.9%
t749
 
5.5%
o674
 
5.0%
Other values (11)3440
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12433
92.0%
Dash Punctuation1088
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1543
12.4%
r1379
11.1%
a1062
 
8.5%
s986
 
7.9%
i922
 
7.4%
c886
 
7.1%
p792
 
6.4%
t749
 
6.0%
o674
 
5.4%
n620
 
5.0%
Other values (10)2820
22.7%
Dash Punctuation
ValueCountFrequency (%)
-1088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12433
92.0%
Common1088
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1543
12.4%
r1379
11.1%
a1062
 
8.5%
s986
 
7.9%
i922
 
7.4%
c886
 
7.1%
p792
 
6.4%
t749
 
6.0%
o674
 
5.4%
n620
 
5.0%
Other values (10)2820
22.7%
Common
ValueCountFrequency (%)
-1088
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1543
11.4%
r1379
10.2%
-1088
 
8.0%
a1062
 
7.9%
s986
 
7.3%
i922
 
6.8%
c886
 
6.6%
p792
 
5.9%
t749
 
5.5%
o674
 
5.0%
Other values (11)3440
25.4%

insured_hobbies
Categorical

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
reading
 
64
exercise
 
57
paintball
 
57
bungie-jumping
 
56
movies
 
55
Other values (15)
711 

Length

Max length14
Median length11
Mean length8.113
Min length4

Characters and Unicode

Total characters8113
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsleeping
2nd rowreading
3rd rowboard-games
4th rowboard-games
5th rowboard-games

Common Values

ValueCountFrequency (%)
reading64
 
6.4%
exercise57
 
5.7%
paintball57
 
5.7%
bungie-jumping56
 
5.6%
movies55
 
5.5%
golf55
 
5.5%
camping55
 
5.5%
kayaking54
 
5.4%
yachting53
 
5.3%
hiking52
 
5.2%
Other values (10)442
44.2%

Length

2022-06-26T10:05:16.986656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reading64
 
6.4%
exercise57
 
5.7%
paintball57
 
5.7%
bungie-jumping56
 
5.6%
movies55
 
5.5%
golf55
 
5.5%
camping55
 
5.5%
kayaking54
 
5.4%
yachting53
 
5.3%
hiking52
 
5.2%
Other values (10)442
44.2%

Most occurring characters

ValueCountFrequency (%)
i927
 
11.4%
g725
 
8.9%
e705
 
8.7%
a700
 
8.6%
n672
 
8.3%
s545
 
6.7%
o337
 
4.2%
l325
 
4.0%
m313
 
3.9%
p305
 
3.8%
Other values (14)2559
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7875
97.1%
Dash Punctuation238
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i927
 
11.8%
g725
 
9.2%
e705
 
9.0%
a700
 
8.9%
n672
 
8.5%
s545
 
6.9%
o337
 
4.3%
l325
 
4.1%
m313
 
4.0%
p305
 
3.9%
Other values (13)2321
29.5%
Dash Punctuation
ValueCountFrequency (%)
-238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7875
97.1%
Common238
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i927
 
11.8%
g725
 
9.2%
e705
 
9.0%
a700
 
8.9%
n672
 
8.5%
s545
 
6.9%
o337
 
4.3%
l325
 
4.1%
m313
 
4.0%
p305
 
3.9%
Other values (13)2321
29.5%
Common
ValueCountFrequency (%)
-238
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i927
 
11.4%
g725
 
8.9%
e705
 
8.7%
a700
 
8.6%
n672
 
8.3%
s545
 
6.7%
o337
 
4.2%
l325
 
4.0%
m313
 
3.9%
p305
 
3.8%
Other values (14)2559
31.5%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
own-child
183 
other-relative
177 
not-in-family
174 
husband
170 
wife
155 

Length

Max length14
Median length13
Mean length9.466
Min length4

Characters and Unicode

Total characters9466
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhusband
2nd rowother-relative
3rd rowown-child
4th rowunmarried
5th rowunmarried

Common Values

ValueCountFrequency (%)
own-child183
18.3%
other-relative177
17.7%
not-in-family174
17.4%
husband170
17.0%
wife155
15.5%
unmarried141
14.1%

Length

2022-06-26T10:05:17.334218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:17.859775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
own-child183
18.3%
other-relative177
17.7%
not-in-family174
17.4%
husband170
17.0%
wife155
15.5%
unmarried141
14.1%

Most occurring characters

ValueCountFrequency (%)
i1004
 
10.6%
n842
 
8.9%
e827
 
8.7%
-708
 
7.5%
a662
 
7.0%
r636
 
6.7%
l534
 
5.6%
o534
 
5.6%
h530
 
5.6%
t528
 
5.6%
Other values (10)2661
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8758
92.5%
Dash Punctuation708
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1004
11.5%
n842
 
9.6%
e827
 
9.4%
a662
 
7.6%
r636
 
7.3%
l534
 
6.1%
o534
 
6.1%
h530
 
6.1%
t528
 
6.0%
d494
 
5.6%
Other values (9)2167
24.7%
Dash Punctuation
ValueCountFrequency (%)
-708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8758
92.5%
Common708
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1004
11.5%
n842
 
9.6%
e827
 
9.4%
a662
 
7.6%
r636
 
7.3%
l534
 
6.1%
o534
 
6.1%
h530
 
6.1%
t528
 
6.0%
d494
 
5.6%
Other values (9)2167
24.7%
Common
ValueCountFrequency (%)
-708
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1004
 
10.6%
n842
 
8.9%
e827
 
8.7%
-708
 
7.5%
a662
 
7.0%
r636
 
6.7%
l534
 
5.6%
o534
 
5.6%
h530
 
5.6%
t528
 
5.6%
Other values (10)2661
28.1%

capital-gains
Real number (ℝ≥0)

ZEROS

Distinct338
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25126.1
Minimum0
Maximum100500
Zeros508
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:18.217519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351025
95-th percentile70615
Maximum100500
Range100500
Interquartile range (IQR)51025

Descriptive statistics

Standard deviation27872.18771
Coefficient of variation (CV)1.109292238
Kurtosis-1.276703511
Mean25126.1
Median Absolute Deviation (MAD)0
Skewness0.4788502296
Sum25126100
Variance776858847.6
MonotonicityNot monotonic
2022-06-26T10:05:18.522246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0508
50.8%
463005
 
0.5%
515004
 
0.4%
685004
 
0.4%
556003
 
0.3%
497003
 
0.3%
517003
 
0.3%
567003
 
0.3%
476003
 
0.3%
440003
 
0.3%
Other values (328)461
46.1%
ValueCountFrequency (%)
0508
50.8%
8001
 
0.1%
100001
 
0.1%
110001
 
0.1%
121001
 
0.1%
128001
 
0.1%
131001
 
0.1%
141001
 
0.1%
161001
 
0.1%
173001
 
0.1%
ValueCountFrequency (%)
1005001
0.1%
988001
0.1%
948001
0.1%
919001
0.1%
907001
0.1%
888001
0.1%
884001
0.1%
878001
0.1%
849001
0.1%
839001
0.1%

capital-loss
Real number (ℝ)

ZEROS

Distinct354
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26793.7
Minimum-111100
Maximum0
Zeros475
Zeros (%)47.5%
Negative525
Negative (%)52.5%
Memory size7.9 KiB
2022-06-26T10:05:19.016562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72305
Q1-51500
median-23250
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)51500

Descriptive statistics

Standard deviation28104.09669
Coefficient of variation (CV)-1.048906896
Kurtosis-1.3138745
Mean-26793.7
Median Absolute Deviation (MAD)23250
Skewness-0.391471943
Sum-26793700
Variance789840250.6
MonotonicityNot monotonic
2022-06-26T10:05:19.649077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0475
47.5%
-317005
 
0.5%
-537005
 
0.5%
-503005
 
0.5%
-453004
 
0.4%
-510004
 
0.4%
-328004
 
0.4%
-538004
 
0.4%
-492004
 
0.4%
-314004
 
0.4%
Other values (344)486
48.6%
ValueCountFrequency (%)
-1111001
0.1%
-936001
0.1%
-914001
0.1%
-912001
0.1%
-906001
0.1%
-902001
0.1%
-901001
0.1%
-894001
0.1%
-883001
0.1%
-873001
0.1%
ValueCountFrequency (%)
0475
47.5%
-57001
 
0.1%
-63001
 
0.1%
-85001
 
0.1%
-106001
 
0.1%
-121001
 
0.1%
-132001
 
0.1%
-138001
 
0.1%
-156001
 
0.1%
-157002
 
0.2%

incident_date
Categorical

HIGH CARDINALITY

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2015-02-02
 
28
2015-02-17
 
26
2015-01-07
 
25
2015-01-10
 
24
2015-02-04
 
24
Other values (55)
873 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-01-25
2nd row2015-01-21
3rd row2015-02-22
4th row2015-01-10
5th row2015-02-17

Common Values

ValueCountFrequency (%)
2015-02-0228
 
2.8%
2015-02-1726
 
2.6%
2015-01-0725
 
2.5%
2015-01-1024
 
2.4%
2015-02-0424
 
2.4%
2015-01-2424
 
2.4%
2015-01-1923
 
2.3%
2015-01-0822
 
2.2%
2015-01-1321
 
2.1%
2015-01-3021
 
2.1%
Other values (50)762
76.2%

Length

2022-06-26T10:05:19.966650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-02-0228
 
2.8%
2015-02-1726
 
2.6%
2015-01-0725
 
2.5%
2015-02-0424
 
2.4%
2015-01-2424
 
2.4%
2015-01-1024
 
2.4%
2015-01-1923
 
2.3%
2015-01-0822
 
2.2%
2015-01-1321
 
2.1%
2015-01-3021
 
2.1%
Other values (50)762
76.2%

Most occurring characters

ValueCountFrequency (%)
02406
24.1%
-2000
20.0%
11978
19.8%
21888
18.9%
51082
10.8%
3153
 
1.5%
4114
 
1.1%
8105
 
1.1%
7103
 
1.0%
697
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8000
80.0%
Dash Punctuation2000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02406
30.1%
11978
24.7%
21888
23.6%
51082
13.5%
3153
 
1.9%
4114
 
1.4%
8105
 
1.3%
7103
 
1.3%
697
 
1.2%
974
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02406
24.1%
-2000
20.0%
11978
19.8%
21888
18.9%
51082
10.8%
3153
 
1.5%
4114
 
1.1%
8105
 
1.1%
7103
 
1.0%
697
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02406
24.1%
-2000
20.0%
11978
19.8%
21888
18.9%
51082
10.8%
3153
 
1.5%
4114
 
1.1%
8105
 
1.1%
7103
 
1.0%
697
 
1.0%

incident_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Multi-vehicle Collision
419 
Single Vehicle Collision
403 
Vehicle Theft
94 
Parked Car
84 

Length

Max length24
Median length23
Mean length21.371
Min length10

Characters and Unicode

Total characters21371
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle Vehicle Collision
2nd rowVehicle Theft
3rd rowMulti-vehicle Collision
4th rowSingle Vehicle Collision
5th rowVehicle Theft

Common Values

ValueCountFrequency (%)
Multi-vehicle Collision419
41.9%
Single Vehicle Collision403
40.3%
Vehicle Theft94
 
9.4%
Parked Car84
 
8.4%

Length

2022-06-26T10:05:20.347041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:21.453409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
collision822
34.2%
vehicle497
20.7%
multi-vehicle419
17.4%
single403
16.8%
theft94
 
3.9%
parked84
 
3.5%
car84
 
3.5%

Most occurring characters

ValueCountFrequency (%)
l3382
15.8%
i3382
15.8%
e2413
11.3%
o1644
 
7.7%
1403
 
6.6%
n1225
 
5.7%
h1010
 
4.7%
c916
 
4.3%
C906
 
4.2%
s822
 
3.8%
Other values (15)4268
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17146
80.2%
Uppercase Letter2403
 
11.2%
Space Separator1403
 
6.6%
Dash Punctuation419
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l3382
19.7%
i3382
19.7%
e2413
14.1%
o1644
9.6%
n1225
 
7.1%
h1010
 
5.9%
c916
 
5.3%
s822
 
4.8%
t513
 
3.0%
u419
 
2.4%
Other values (7)1420
8.3%
Uppercase Letter
ValueCountFrequency (%)
C906
37.7%
V497
20.7%
M419
17.4%
S403
16.8%
T94
 
3.9%
P84
 
3.5%
Space Separator
ValueCountFrequency (%)
1403
100.0%
Dash Punctuation
ValueCountFrequency (%)
-419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19549
91.5%
Common1822
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l3382
17.3%
i3382
17.3%
e2413
12.3%
o1644
8.4%
n1225
 
6.3%
h1010
 
5.2%
c916
 
4.7%
C906
 
4.6%
s822
 
4.2%
t513
 
2.6%
Other values (13)3336
17.1%
Common
ValueCountFrequency (%)
1403
77.0%
-419
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l3382
15.8%
i3382
15.8%
e2413
11.3%
o1644
 
7.7%
1403
 
6.6%
n1225
 
5.7%
h1010
 
4.7%
c916
 
4.3%
C906
 
4.2%
s822
 
3.8%
Other values (15)4268
20.0%

collision_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Rear Collision
292 
Side Collision
276 
Front Collision
254 
?
178 

Length

Max length15
Median length14
Mean length11.94
Min length1

Characters and Unicode

Total characters11940
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSide Collision
2nd row?
3rd rowRear Collision
4th rowFront Collision
5th row?

Common Values

ValueCountFrequency (%)
Rear Collision292
29.2%
Side Collision276
27.6%
Front Collision254
25.4%
?178
17.8%

Length

2022-06-26T10:05:21.827717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:22.108867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
collision822
45.1%
rear292
 
16.0%
side276
 
15.1%
front254
 
13.9%
178
 
9.8%

Most occurring characters

ValueCountFrequency (%)
i1920
16.1%
o1898
15.9%
l1644
13.8%
n1076
9.0%
822
6.9%
C822
6.9%
s822
6.9%
e568
 
4.8%
r546
 
4.6%
R292
 
2.4%
Other values (6)1530
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9296
77.9%
Uppercase Letter1644
 
13.8%
Space Separator822
 
6.9%
Other Punctuation178
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1920
20.7%
o1898
20.4%
l1644
17.7%
n1076
11.6%
s822
8.8%
e568
 
6.1%
r546
 
5.9%
a292
 
3.1%
d276
 
3.0%
t254
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C822
50.0%
R292
 
17.8%
S276
 
16.8%
F254
 
15.5%
Space Separator
ValueCountFrequency (%)
822
100.0%
Other Punctuation
ValueCountFrequency (%)
?178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10940
91.6%
Common1000
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1920
17.6%
o1898
17.3%
l1644
15.0%
n1076
9.8%
C822
7.5%
s822
7.5%
e568
 
5.2%
r546
 
5.0%
R292
 
2.7%
a292
 
2.7%
Other values (4)1060
9.7%
Common
ValueCountFrequency (%)
822
82.2%
?178
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1920
16.1%
o1898
15.9%
l1644
13.8%
n1076
9.0%
822
6.9%
C822
6.9%
s822
6.9%
e568
 
4.8%
r546
 
4.6%
R292
 
2.4%
Other values (6)1530
12.8%

incident_severity
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minor Damage
354 
Total Loss
280 
Major Damage
276 
Trivial Damage
90 

Length

Max length14
Median length12
Mean length11.62
Min length10

Characters and Unicode

Total characters11620
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor Damage
2nd rowMinor Damage
3rd rowMinor Damage
4th rowMajor Damage
5th rowMinor Damage

Common Values

ValueCountFrequency (%)
Minor Damage354
35.4%
Total Loss280
28.0%
Major Damage276
27.6%
Trivial Damage90
 
9.0%

Length

2022-06-26T10:05:22.696849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:23.385835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
damage720
36.0%
minor354
17.7%
total280
 
14.0%
loss280
 
14.0%
major276
 
13.8%
trivial90
 
4.5%

Most occurring characters

ValueCountFrequency (%)
a2086
18.0%
o1190
10.2%
1000
 
8.6%
g720
 
6.2%
m720
 
6.2%
e720
 
6.2%
r720
 
6.2%
D720
 
6.2%
M630
 
5.4%
s560
 
4.8%
Other values (8)2554
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8620
74.2%
Uppercase Letter2000
 
17.2%
Space Separator1000
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2086
24.2%
o1190
13.8%
g720
 
8.4%
m720
 
8.4%
e720
 
8.4%
r720
 
8.4%
s560
 
6.5%
i534
 
6.2%
l370
 
4.3%
n354
 
4.1%
Other values (3)646
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
D720
36.0%
M630
31.5%
T370
18.5%
L280
 
14.0%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10620
91.4%
Common1000
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2086
19.6%
o1190
11.2%
g720
 
6.8%
m720
 
6.8%
e720
 
6.8%
r720
 
6.8%
D720
 
6.8%
M630
 
5.9%
s560
 
5.3%
i534
 
5.0%
Other values (7)2020
19.0%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2086
18.0%
o1190
10.2%
1000
 
8.6%
g720
 
6.2%
m720
 
6.2%
e720
 
6.2%
r720
 
6.2%
D720
 
6.2%
M630
 
5.4%
s560
 
4.8%
Other values (8)2554
22.0%

authorities_contacted
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Police
292 
Fire
223 
Other
198 
Ambulance
196 
None
91 

Length

Max length9
Median length6
Mean length5.762
Min length4

Characters and Unicode

Total characters5762
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolice
2nd rowPolice
3rd rowPolice
4th rowPolice
5th rowNone

Common Values

ValueCountFrequency (%)
Police292
29.2%
Fire223
22.3%
Other198
19.8%
Ambulance196
19.6%
None91
 
9.1%

Length

2022-06-26T10:05:23.661219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:24.305340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
police292
29.2%
fire223
22.3%
other198
19.8%
ambulance196
19.6%
none91
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e1000
17.4%
i515
 
8.9%
l488
 
8.5%
c488
 
8.5%
r421
 
7.3%
o383
 
6.6%
P292
 
5.1%
n287
 
5.0%
F223
 
3.9%
h198
 
3.4%
Other values (8)1467
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4762
82.6%
Uppercase Letter1000
 
17.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1000
21.0%
i515
10.8%
l488
10.2%
c488
10.2%
r421
8.8%
o383
 
8.0%
n287
 
6.0%
h198
 
4.2%
t198
 
4.2%
m196
 
4.1%
Other values (3)588
12.3%
Uppercase Letter
ValueCountFrequency (%)
P292
29.2%
F223
22.3%
O198
19.8%
A196
19.6%
N91
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin5762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1000
17.4%
i515
 
8.9%
l488
 
8.5%
c488
 
8.5%
r421
 
7.3%
o383
 
6.6%
P292
 
5.1%
n287
 
5.0%
F223
 
3.9%
h198
 
3.4%
Other values (8)1467
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1000
17.4%
i515
 
8.9%
l488
 
8.5%
c488
 
8.5%
r421
 
7.3%
o383
 
6.6%
P292
 
5.1%
n287
 
5.0%
F223
 
3.9%
h198
 
3.4%
Other values (8)1467
25.5%

incident_state
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
NY
262 
SC
248 
WV
217 
VA
110 
NC
110 
Other values (2)
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC
2nd rowVA
3rd rowNY
4th rowOH
5th rowNY

Common Values

ValueCountFrequency (%)
NY262
26.2%
SC248
24.8%
WV217
21.7%
VA110
11.0%
NC110
11.0%
PA30
 
3.0%
OH23
 
2.3%

Length

2022-06-26T10:05:25.078450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:25.455971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ny262
26.2%
sc248
24.8%
wv217
21.7%
va110
11.0%
nc110
11.0%
pa30
 
3.0%
oh23
 
2.3%

Most occurring characters

ValueCountFrequency (%)
N372
18.6%
C358
17.9%
V327
16.4%
Y262
13.1%
S248
12.4%
W217
10.8%
A140
 
7.0%
P30
 
1.5%
O23
 
1.1%
H23
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N372
18.6%
C358
17.9%
V327
16.4%
Y262
13.1%
S248
12.4%
W217
10.8%
A140
 
7.0%
P30
 
1.5%
O23
 
1.1%
H23
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N372
18.6%
C358
17.9%
V327
16.4%
Y262
13.1%
S248
12.4%
W217
10.8%
A140
 
7.0%
P30
 
1.5%
O23
 
1.1%
H23
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N372
18.6%
C358
17.9%
V327
16.4%
Y262
13.1%
S248
12.4%
W217
10.8%
A140
 
7.0%
P30
 
1.5%
O23
 
1.1%
H23
 
1.1%

incident_city
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Springfield
157 
Arlington
152 
Columbus
149 
Northbend
145 
Hillsdale
141 
Other values (2)
256 

Length

Max length11
Median length9
Mean length9.287
Min length8

Characters and Unicode

Total characters9287
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColumbus
2nd rowRiverwood
3rd rowColumbus
4th rowArlington
5th rowArlington

Common Values

ValueCountFrequency (%)
Springfield157
15.7%
Arlington152
15.2%
Columbus149
14.9%
Northbend145
14.5%
Hillsdale141
14.1%
Riverwood134
13.4%
Northbrook122
12.2%

Length

2022-06-26T10:05:25.947217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:26.357892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
springfield157
15.7%
arlington152
15.2%
columbus149
14.9%
northbend145
14.5%
hillsdale141
14.1%
riverwood134
13.4%
northbrook122
12.2%

Most occurring characters

ValueCountFrequency (%)
o1080
 
11.6%
l881
 
9.5%
r832
 
9.0%
i741
 
8.0%
n606
 
6.5%
d577
 
6.2%
e577
 
6.2%
t419
 
4.5%
b416
 
4.5%
g309
 
3.3%
Other values (16)2849
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8287
89.2%
Uppercase Letter1000
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1080
13.0%
l881
10.6%
r832
10.0%
i741
8.9%
n606
 
7.3%
d577
 
7.0%
e577
 
7.0%
t419
 
5.1%
b416
 
5.0%
g309
 
3.7%
Other values (10)1849
22.3%
Uppercase Letter
ValueCountFrequency (%)
N267
26.7%
S157
15.7%
A152
15.2%
C149
14.9%
H141
14.1%
R134
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin9287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1080
 
11.6%
l881
 
9.5%
r832
 
9.0%
i741
 
8.0%
n606
 
6.5%
d577
 
6.2%
e577
 
6.2%
t419
 
4.5%
b416
 
4.5%
g309
 
3.3%
Other values (16)2849
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o1080
 
11.6%
l881
 
9.5%
r832
 
9.0%
i741
 
8.0%
n606
 
6.5%
d577
 
6.2%
e577
 
6.2%
t419
 
4.5%
b416
 
4.5%
g309
 
3.3%
Other values (16)2849
30.7%

incident_location
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
9935 4th Drive
 
1
4214 MLK Ridge
 
1
8548 Cherokee Ridge
 
1
2352 MLK Drive
 
1
9734 2nd Ridge
 
1
Other values (995)
995 

Length

Max length23
Median length20
Mean length14.749
Min length11

Characters and Unicode

Total characters14749
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row9935 4th Drive
2nd row6608 MLK Hwy
3rd row7121 Francis Lane
4th row6956 Maple Drive
5th row3041 3rd Ave

Common Values

ValueCountFrequency (%)
9935 4th Drive1
 
0.1%
4214 MLK Ridge1
 
0.1%
8548 Cherokee Ridge1
 
0.1%
2352 MLK Drive1
 
0.1%
9734 2nd Ridge1
 
0.1%
3122 Apache Drive1
 
0.1%
9816 Britain St1
 
0.1%
8214 Flute St1
 
0.1%
6259 Lincoln Hwy1
 
0.1%
4492 Andromedia Ave1
 
0.1%
Other values (990)990
99.0%

Length

2022-06-26T10:05:26.752648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drive173
 
5.8%
lane171
 
5.7%
ridge171
 
5.7%
st171
 
5.7%
ave161
 
5.4%
hwy153
 
5.1%
4th57
 
1.9%
5th52
 
1.7%
texas47
 
1.6%
francis45
 
1.5%
Other values (961)1799
60.0%

Most occurring characters

ValueCountFrequency (%)
2000
 
13.6%
e1236
 
8.4%
i629
 
4.3%
a603
 
4.1%
n518
 
3.5%
r491
 
3.3%
t474
 
3.2%
5467
 
3.2%
1443
 
3.0%
4441
 
3.0%
Other values (39)7447
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6659
45.1%
Decimal Number4201
28.5%
Space Separator2000
 
13.6%
Uppercase Letter1889
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1236
18.6%
i629
9.4%
a603
 
9.1%
n518
 
7.8%
r491
 
7.4%
t474
 
7.1%
v377
 
5.7%
d322
 
4.8%
o255
 
3.8%
h212
 
3.2%
Other values (12)1542
23.2%
Uppercase Letter
ValueCountFrequency (%)
L258
13.7%
S237
12.5%
A230
12.2%
R205
10.9%
D173
9.2%
H153
8.1%
T89
 
4.7%
F86
 
4.6%
M83
 
4.4%
W80
 
4.2%
Other values (6)295
15.6%
Decimal Number
ValueCountFrequency (%)
5467
11.1%
1443
10.5%
4441
10.5%
3441
10.5%
8437
10.4%
2430
10.2%
9428
10.2%
7425
10.1%
6404
9.6%
0285
6.8%
Space Separator
ValueCountFrequency (%)
2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8548
58.0%
Common6201
42.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1236
 
14.5%
i629
 
7.4%
a603
 
7.1%
n518
 
6.1%
r491
 
5.7%
t474
 
5.5%
v377
 
4.4%
d322
 
3.8%
L258
 
3.0%
o255
 
3.0%
Other values (28)3385
39.6%
Common
ValueCountFrequency (%)
2000
32.3%
5467
 
7.5%
1443
 
7.1%
4441
 
7.1%
3441
 
7.1%
8437
 
7.0%
2430
 
6.9%
9428
 
6.9%
7425
 
6.9%
6404
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII14749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2000
 
13.6%
e1236
 
8.4%
i629
 
4.3%
a603
 
4.1%
n518
 
3.5%
r491
 
3.3%
t474
 
3.2%
5467
 
3.2%
1443
 
3.0%
4441
 
3.0%
Other values (39)7447
50.5%

incident_hour_of_the_day
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.644
Minimum0
Maximum23
Zeros52
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:27.016196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.951372928
Coefficient of variation (CV)0.5969918351
Kurtosis-1.192940152
Mean11.644
Median Absolute Deviation (MAD)6
Skewness-0.03558446644
Sum11644
Variance48.32158559
MonotonicityNot monotonic
2022-06-26T10:05:27.315148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1754
 
5.4%
353
 
5.3%
052
 
5.2%
2351
 
5.1%
1649
 
4.9%
1346
 
4.6%
1046
 
4.6%
446
 
4.6%
644
 
4.4%
943
 
4.3%
Other values (14)516
51.6%
ValueCountFrequency (%)
052
5.2%
129
2.9%
231
3.1%
353
5.3%
446
4.6%
533
3.3%
644
4.4%
740
4.0%
836
3.6%
943
4.3%
ValueCountFrequency (%)
2351
5.1%
2238
3.8%
2142
4.2%
2034
3.4%
1940
4.0%
1841
4.1%
1754
5.4%
1649
4.9%
1539
3.9%
1443
4.3%

number_of_vehicles_involved
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
581 
3
358 
4
 
31
2
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1581
58.1%
3358
35.8%
431
 
3.1%
230
 
3.0%

Length

2022-06-26T10:05:27.582985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:27.894523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1581
58.1%
3358
35.8%
431
 
3.1%
230
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1581
58.1%
3358
35.8%
431
 
3.1%
230
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1581
58.1%
3358
35.8%
431
 
3.1%
230
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1581
58.1%
3358
35.8%
431
 
3.1%
230
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1581
58.1%
3358
35.8%
431
 
3.1%
230
 
3.0%

property_damage
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
?
360 
NO
338 
YES
302 

Length

Max length3
Median length2
Mean length1.942
Min length1

Characters and Unicode

Total characters1942
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th row?
5th rowNO

Common Values

ValueCountFrequency (%)
?360
36.0%
NO338
33.8%
YES302
30.2%

Length

2022-06-26T10:05:28.207066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:28.572296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
360
36.0%
no338
33.8%
yes302
30.2%

Most occurring characters

ValueCountFrequency (%)
?360
18.5%
N338
17.4%
O338
17.4%
Y302
15.6%
E302
15.6%
S302
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1582
81.5%
Other Punctuation360
 
18.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N338
21.4%
O338
21.4%
Y302
19.1%
E302
19.1%
S302
19.1%
Other Punctuation
ValueCountFrequency (%)
?360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1582
81.5%
Common360
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N338
21.4%
O338
21.4%
Y302
19.1%
E302
19.1%
S302
19.1%
Common
ValueCountFrequency (%)
?360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
?360
18.5%
N338
17.4%
O338
17.4%
Y302
15.6%
E302
15.6%
S302
15.6%

bodily_injuries
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
340 
2
332 
1
328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0340
34.0%
2332
33.2%
1328
32.8%

Length

2022-06-26T10:05:28.942358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:29.248834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0340
34.0%
2332
33.2%
1328
32.8%

Most occurring characters

ValueCountFrequency (%)
0340
34.0%
2332
33.2%
1328
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0340
34.0%
2332
33.2%
1328
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0340
34.0%
2332
33.2%
1328
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0340
34.0%
2332
33.2%
1328
32.8%

witnesses
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
258 
2
250 
0
249 
3
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1258
25.8%
2250
25.0%
0249
24.9%
3243
24.3%

Length

2022-06-26T10:05:29.554805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:29.910611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1258
25.8%
2250
25.0%
0249
24.9%
3243
24.3%

Most occurring characters

ValueCountFrequency (%)
1258
25.8%
2250
25.0%
0249
24.9%
3243
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1258
25.8%
2250
25.0%
0249
24.9%
3243
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1258
25.8%
2250
25.0%
0249
24.9%
3243
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1258
25.8%
2250
25.0%
0249
24.9%
3243
24.3%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
?
343 
NO
343 
YES
314 

Length

Max length3
Median length2
Mean length1.971
Min length1

Characters and Unicode

Total characters1971
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
?343
34.3%
NO343
34.3%
YES314
31.4%

Length

2022-06-26T10:05:30.254878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-26T10:05:30.576756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
343
34.3%
no343
34.3%
yes314
31.4%

Most occurring characters

ValueCountFrequency (%)
?343
17.4%
N343
17.4%
O343
17.4%
Y314
15.9%
E314
15.9%
S314
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1628
82.6%
Other Punctuation343
 
17.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N343
21.1%
O343
21.1%
Y314
19.3%
E314
19.3%
S314
19.3%
Other Punctuation
ValueCountFrequency (%)
?343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1628
82.6%
Common343
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N343
21.1%
O343
21.1%
Y314
19.3%
E314
19.3%
S314
19.3%
Common
ValueCountFrequency (%)
?343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
?343
17.4%
N343
17.4%
O343
17.4%
Y314
15.9%
E314
15.9%
S314
15.9%

total_claim_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct763
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52761.94
Minimum100
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:30.985851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4320
Q141812.5
median58055
Q370592.5
95-th percentile88413
Maximum114920
Range114820
Interquartile range (IQR)28780

Descriptive statistics

Standard deviation26401.53319
Coefficient of variation (CV)0.5003897353
Kurtosis-0.4540814267
Mean52761.94
Median Absolute Deviation (MAD)13855
Skewness-0.5945819885
Sum52761940
Variance697040954.8
MonotonicityNot monotonic
2022-06-26T10:05:31.461637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
594005
 
0.5%
26404
 
0.4%
704004
 
0.4%
43204
 
0.4%
442004
 
0.4%
754004
 
0.4%
606004
 
0.4%
31904
 
0.4%
585004
 
0.4%
702904
 
0.4%
Other values (753)959
95.9%
ValueCountFrequency (%)
1001
 
0.1%
19201
 
0.1%
21601
 
0.1%
22501
 
0.1%
24001
 
0.1%
25201
 
0.1%
26404
0.4%
27002
0.2%
28001
 
0.1%
28601
 
0.1%
ValueCountFrequency (%)
1149201
0.1%
1123201
0.1%
1084801
0.1%
1080301
0.1%
1079001
0.1%
1058201
0.1%
1050401
0.1%
1046101
0.1%
1035601
0.1%
1018601
0.1%

injury_claim
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct638
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7433.42
Minimum0
Maximum21450
Zeros25
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:31.945098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14295
median6775
Q311305
95-th percentile15662
Maximum21450
Range21450
Interquartile range (IQR)7010

Descriptive statistics

Standard deviation4880.951853
Coefficient of variation (CV)0.6566226385
Kurtosis-0.7630870611
Mean7433.42
Median Absolute Deviation (MAD)3705
Skewness0.2648108785
Sum7433420
Variance23823690.99
MonotonicityNot monotonic
2022-06-26T10:05:32.401834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025
 
2.5%
6407
 
0.7%
4807
 
0.7%
6605
 
0.5%
5805
 
0.5%
135205
 
0.5%
11805
 
0.5%
8605
 
0.5%
63405
 
0.5%
7805
 
0.5%
Other values (628)926
92.6%
ValueCountFrequency (%)
025
2.5%
101
 
0.1%
2201
 
0.1%
2501
 
0.1%
2802
 
0.2%
2901
 
0.1%
3003
 
0.3%
3302
 
0.2%
3501
 
0.1%
3601
 
0.1%
ValueCountFrequency (%)
214501
0.1%
213301
0.1%
207001
0.1%
190201
0.1%
185201
0.1%
182201
0.1%
181801
0.1%
180801
0.1%
180001
0.1%
178801
0.1%

property_claim
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct626
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7399.57
Minimum0
Maximum23670
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:32.833698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14445
median6750
Q310885
95-th percentile15540
Maximum23670
Range23670
Interquartile range (IQR)6440

Descriptive statistics

Standard deviation4824.726179
Coefficient of variation (CV)0.6520279122
Kurtosis-0.3763863117
Mean7399.57
Median Absolute Deviation (MAD)3290
Skewness0.3781687764
Sum7399570
Variance23277982.7
MonotonicityNot monotonic
2022-06-26T10:05:33.201147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
1.9%
8606
 
0.6%
4805
 
0.5%
6605
 
0.5%
100005
 
0.5%
6405
 
0.5%
6505
 
0.5%
110805
 
0.5%
8404
 
0.4%
53104
 
0.4%
Other values (616)937
93.7%
ValueCountFrequency (%)
019
1.9%
201
 
0.1%
2401
 
0.1%
2501
 
0.1%
2601
 
0.1%
2803
 
0.3%
2902
 
0.2%
3003
 
0.3%
3203
 
0.3%
3301
 
0.1%
ValueCountFrequency (%)
236701
0.1%
218101
0.1%
216301
0.1%
215801
0.1%
212401
0.1%
205501
0.1%
203101
0.1%
202801
0.1%
199501
0.1%
196501
0.1%

vehicle_claim
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct726
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37928.95
Minimum70
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:33.571234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile3273.5
Q130292.5
median42100
Q350822.5
95-th percentile63094.5
Maximum79560
Range79490
Interquartile range (IQR)20530

Descriptive statistics

Standard deviation18886.25289
Coefficient of variation (CV)0.4979376675
Kurtosis-0.4465729231
Mean37928.95
Median Absolute Deviation (MAD)9840
Skewness-0.6210979312
Sum37928950
Variance356690548.3
MonotonicityNot monotonic
2022-06-26T10:05:34.323113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50407
 
0.7%
33606
 
0.6%
520805
 
0.5%
47205
 
0.5%
36005
 
0.5%
448005
 
0.5%
336005
 
0.5%
427204
 
0.4%
415804
 
0.4%
350004
 
0.4%
Other values (716)950
95.0%
ValueCountFrequency (%)
701
0.1%
14402
0.2%
16802
0.2%
17501
0.1%
17601
0.1%
18001
0.1%
19602
0.2%
19801
0.1%
20301
0.1%
20801
0.1%
ValueCountFrequency (%)
795601
0.1%
777601
0.1%
776702
0.2%
764001
0.1%
760001
0.1%
756001
0.1%
755301
0.1%
747901
0.1%
736201
0.1%
732601
0.1%

auto_make
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Saab
80 
Dodge
80 
Suburu
80 
Nissan
78 
Chevrolet
76 
Other values (9)
606 

Length

Max length10
Median length9
Mean length5.703
Min length3

Characters and Unicode

Total characters5703
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaab
2nd rowMercedes
3rd rowDodge
4th rowChevrolet
5th rowAccura

Common Values

ValueCountFrequency (%)
Saab80
 
8.0%
Dodge80
 
8.0%
Suburu80
 
8.0%
Nissan78
 
7.8%
Chevrolet76
 
7.6%
Ford72
 
7.2%
BMW72
 
7.2%
Toyota70
 
7.0%
Audi69
 
6.9%
Accura68
 
6.8%
Other values (4)255
25.5%

Length

2022-06-26T10:05:34.726706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saab80
 
8.0%
dodge80
 
8.0%
suburu80
 
8.0%
nissan78
 
7.8%
chevrolet76
 
7.6%
ford72
 
7.2%
bmw72
 
7.2%
toyota70
 
7.0%
audi69
 
6.9%
accura68
 
6.8%
Other values (4)255
25.5%

Most occurring characters

ValueCountFrequency (%)
e629
 
11.0%
a499
 
8.7%
o491
 
8.6%
u377
 
6.6%
r361
 
6.3%
d341
 
6.0%
s289
 
5.1%
c201
 
3.5%
n201
 
3.5%
S160
 
2.8%
Other values (23)2154
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4559
79.9%
Uppercase Letter1144
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e629
13.8%
a499
10.9%
o491
10.8%
u377
 
8.3%
r361
 
7.9%
d341
 
7.5%
s289
 
6.3%
c201
 
4.4%
n201
 
4.4%
b160
 
3.5%
Other values (10)1010
22.2%
Uppercase Letter
ValueCountFrequency (%)
S160
14.0%
M137
12.0%
A137
12.0%
D80
7.0%
N78
6.8%
C76
 
6.6%
B72
 
6.3%
F72
 
6.3%
W72
 
6.3%
T70
 
6.1%
Other values (3)190
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin5703
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e629
 
11.0%
a499
 
8.7%
o491
 
8.6%
u377
 
6.6%
r361
 
6.3%
d341
 
6.0%
s289
 
5.1%
c201
 
3.5%
n201
 
3.5%
S160
 
2.8%
Other values (23)2154
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e629
 
11.0%
a499
 
8.7%
o491
 
8.6%
u377
 
6.6%
r361
 
6.3%
d341
 
6.0%
s289
 
5.1%
c201
 
3.5%
n201
 
3.5%
S160
 
2.8%
Other values (23)2154
37.8%

auto_model
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
RAM
 
43
Wrangler
 
42
A3
 
37
Neon
 
37
MDX
 
36
Other values (34)
805 

Length

Max length14
Median length9
Mean length5.178
Min length2

Characters and Unicode

Total characters5178
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row92x
2nd rowE400
3rd rowRAM
4th rowTahoe
5th rowRSX

Common Values

ValueCountFrequency (%)
RAM43
 
4.3%
Wrangler42
 
4.2%
A337
 
3.7%
Neon37
 
3.7%
MDX36
 
3.6%
Jetta35
 
3.5%
Passat33
 
3.3%
A532
 
3.2%
Legacy32
 
3.2%
Pathfinder31
 
3.1%
Other values (29)642
64.2%

Length

2022-06-26T10:05:35.012922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ram43
 
4.1%
wrangler42
 
4.0%
a337
 
3.5%
neon37
 
3.5%
mdx36
 
3.5%
jetta35
 
3.4%
passat33
 
3.2%
a532
 
3.1%
legacy32
 
3.1%
pathfinder31
 
3.0%
Other values (31)685
65.7%

Most occurring characters

ValueCountFrequency (%)
a492
 
9.5%
e428
 
8.3%
r392
 
7.6%
o238
 
4.6%
i235
 
4.5%
t185
 
3.6%
l179
 
3.5%
n178
 
3.4%
M168
 
3.2%
s157
 
3.0%
Other values (42)2526
48.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3351
64.7%
Uppercase Letter1207
 
23.3%
Decimal Number577
 
11.1%
Space Separator43
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a492
14.7%
e428
12.8%
r392
11.7%
o238
 
7.1%
i235
 
7.0%
t185
 
5.5%
l179
 
5.3%
n178
 
5.3%
s157
 
4.7%
d113
 
3.4%
Other values (13)754
22.5%
Uppercase Letter
ValueCountFrequency (%)
M168
13.9%
C133
11.0%
A125
10.4%
X87
 
7.2%
F76
 
6.3%
R75
 
6.2%
L72
 
6.0%
P64
 
5.3%
S52
 
4.3%
E51
 
4.2%
Other values (10)304
25.2%
Decimal Number
ValueCountFrequency (%)
5144
25.0%
0137
23.7%
3118
20.5%
980
13.9%
228
 
4.9%
427
 
4.7%
127
 
4.7%
616
 
2.8%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4558
88.0%
Common620
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a492
 
10.8%
e428
 
9.4%
r392
 
8.6%
o238
 
5.2%
i235
 
5.2%
t185
 
4.1%
l179
 
3.9%
n178
 
3.9%
M168
 
3.7%
s157
 
3.4%
Other values (33)1906
41.8%
Common
ValueCountFrequency (%)
5144
23.2%
0137
22.1%
3118
19.0%
980
12.9%
43
 
6.9%
228
 
4.5%
427
 
4.4%
127
 
4.4%
616
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a492
 
9.5%
e428
 
8.3%
r392
 
7.6%
o238
 
4.6%
i235
 
4.5%
t185
 
3.6%
l179
 
3.5%
n178
 
3.4%
M168
 
3.2%
s157
 
3.0%
Other values (42)2526
48.8%

auto_year
Real number (ℝ≥0)

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.103
Minimum1995
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-06-26T10:05:35.265446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q12000
median2005
Q32010
95-th percentile2014
Maximum2015
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.015860835
Coefficient of variation (CV)0.003000275215
Kurtosis-1.171867756
Mean2005.103
Median Absolute Deviation (MAD)5
Skewness-0.04828880711
Sum2005103
Variance36.19058158
MonotonicityNot monotonic
2022-06-26T10:05:35.558761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
199556
 
5.6%
199955
 
5.5%
200554
 
5.4%
200653
 
5.3%
201153
 
5.3%
200752
 
5.2%
200351
 
5.1%
200950
 
5.0%
201050
 
5.0%
201349
 
4.9%
Other values (11)477
47.7%
ValueCountFrequency (%)
199556
5.6%
199637
3.7%
199746
4.6%
199840
4.0%
199955
5.5%
200042
4.2%
200142
4.2%
200249
4.9%
200351
5.1%
200439
3.9%
ValueCountFrequency (%)
201547
4.7%
201444
4.4%
201349
4.9%
201246
4.6%
201153
5.3%
201050
5.0%
200950
5.0%
200845
4.5%
200752
5.2%
200653
5.3%

fraud_reported
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
753 
True
247 
ValueCountFrequency (%)
False753
75.3%
True247
 
24.7%
2022-06-26T10:05:35.777542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

_c39
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

Interactions

2022-06-26T10:05:00.688448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:52.578176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:56.252559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:00.554897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:05.690245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:11.555677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:15.708229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:20.508135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:24.009181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:28.562900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:35.069319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:42.752521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:47.844324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:55.206570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:01.001962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:52.869984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:56.492898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:00.803288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:06.013123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:11.808763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:16.059148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:20.758382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:24.632965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:28.994722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:35.699104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:43.096258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:48.363825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:55.477984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:01.309491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:53.122988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:56.722564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:01.087295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:06.335912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:12.026632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:16.385535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:21.002440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:24.891273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:29.735717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:36.095166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:43.503469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:48.908304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:56.005613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:01.619028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:53.351986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:57.049974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:01.387113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:06.682323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:12.277440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:16.726627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:21.245372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:25.145685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:30.176669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:36.541046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:43.885434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:49.428524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:56.348997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:01.924971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:53.642461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:57.438770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:01.725593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:07.047563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:12.542345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:17.129784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:21.515934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:25.428219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:30.576445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:37.225280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:44.381391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:49.776212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:56.855172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:02.175019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:53.893094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:57.754716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:02.033250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:07.314111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:12.794741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:17.511852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:21.761660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:25.669379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:30.896508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:37.726264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:44.626482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:50.178852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:57.171622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:02.468064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:54.189401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:58.067015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:02.387326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:07.952390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:13.464204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:17.892932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:22.032685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:25.940639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:31.259594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:38.407485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:45.093454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:50.512067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:57.497675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:02.815432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:54.418513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:58.363426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:02.813982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:08.691510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:13.733110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:18.319517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:22.261166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:26.245473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:31.690771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:38.792430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:45.529336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:51.044436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:58.283354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:03.148736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:54.675316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:58.634663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:03.193204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:09.071075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:13.982263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:18.614134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:22.509233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:26.592197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:32.104842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:39.236510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:45.821347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:51.471852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:58.584359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:03.441387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:54.932517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:58.870935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:03.978837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:09.742001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:14.206394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:18.942996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:22.756053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:26.874845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:32.484129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:39.738592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:46.048292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:52.037086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:59.096980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:03.714607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:55.194128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:59.138262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:04.393024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:10.124100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:14.460102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:19.276024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:23.006558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:27.173476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:32.788950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:40.173651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:46.282736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:52.985811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:59.467576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:04.029426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:55.421248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:59.804499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:04.754361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:10.539845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:14.694851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:19.600633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:23.232291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:27.506305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:33.456065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:41.205816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:46.543296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:53.363503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:59.777874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:04.410779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:55.691938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:00.037298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:05.038570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:10.860562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:14.970169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:19.897392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:23.455228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:27.765701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:34.002143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:41.681921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:46.976349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:53.682118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:00.055274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:04.766374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:03:55.988319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:00.297373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:05.377968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:11.202920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:15.330678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:20.230607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:23.737712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:28.039886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:34.589277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:42.364108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:47.559572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:04:54.237418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-26T10:05:00.402405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-26T10:05:35.996036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-26T10:05:36.513572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-26T10:05:37.138437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-26T10:05:37.798849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-26T10:05:38.533284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-26T10:05:05.602695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-26T10:05:08.447000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-26T10:05:09.040793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
0328485215852014-10-17OH250/50010001406.910466132MALEMDcraft-repairsleepinghusband5330002015-01-25Single Vehicle CollisionSide CollisionMajor DamagePoliceSCColumbus9935 4th Drive51YES12YES7161065101302052080Saab92x2004YNaN
1228423428682006-06-27IN250/50020001197.225000000468176MALEMDmachine-op-inspctreadingother-relative002015-01-21Vehicle Theft?Minor DamagePoliceVARiverwood6608 MLK Hwy81?00?50707807803510MercedesE4002007YNaN
2134296876982000-09-06OH100/30020001413.145000000430632FEMALEPhDsalesboard-gamesown-child3510002015-02-22Multi-vehicle CollisionRear CollisionMinor DamagePoliceNYColumbus7121 Francis Lane73NO23NO346507700385023100DodgeRAM2007NNaN
3256412278111990-05-25IL250/50020001415.746000000608117FEMALEPhDarmed-forcesboard-gamesunmarried48900-624002015-01-10Single Vehicle CollisionFront CollisionMajor DamagePoliceOHArlington6956 Maple Drive51?12NO634006340634050720ChevroletTahoe2014YNaN
4228443674552014-06-06IL500/100010001583.916000000610706MALEAssociatesalesboard-gamesunmarried66000-460002015-02-17Vehicle Theft?Minor DamageNoneNYArlington3041 3rd Ave201NO01NO650013006504550AccuraRSX2009NNaN
5256391045942006-10-12OH250/50010001351.100478456FEMALEPhDtech-supportbungie-jumpingunmarried002015-01-02Multi-vehicle CollisionRear CollisionMajor DamageFireSCArlington8973 Washington St193NO02NO641006410641051280Saab952003YNaN
6137344139782000-06-04IN250/50010001333.350441716MALEPhDprof-specialtyboard-gameshusband0-770002015-01-13Multi-vehicle CollisionFront CollisionMinor DamagePoliceNYSpringfield5846 Weaver Drive03?00?7865021450715050050NissanPathfinder2012NNaN
7165374290271990-02-03IL100/30010001137.030603195MALEAssociatetech-supportbase-jumpingunmarried002015-02-27Multi-vehicle CollisionFront CollisionTotal LossPoliceVAColumbus3525 3rd Hwy233?22YES515909380938032830AudiA52015NNaN
827334856651997-02-05IL100/3005001442.990601734FEMALEPhDother-servicegolfown-child002015-01-30Single Vehicle CollisionFront CollisionTotal LossPoliceWVArlington4872 Rock Ridge211NO11YES277002770277022160ToyotaCamry2012NNaN
9212426365502011-07-25IL100/3005001315.680600983MALEPhDpriv-house-servcampingwife0-393002015-01-05Single Vehicle CollisionRear CollisionTotal LossOtherNCHillsdale3066 Francis Ave141NO21?423004700470032900Saab92x1996NNaN

Last rows

months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
990286436631901994-02-05IL100/3005001564.433000000477644FEMALEMDprof-specialtymoviesunmarried77500-328002015-01-31Single Vehicle CollisionRear CollisionMinor DamageFireNYNorthbrook4755 1st St181?22YES342903810381026670JeepGrand Cherokee2013NNaN
991257441093922006-07-12OH100/30010001280.880433981MALEMDother-servicebasketballother-relative59400-322002015-02-06Single Vehicle CollisionRear CollisionTotal LossOtherWVRiverwood5312 Francis Ridge211NO01NO469800522041760AccuraTL2002NNaN
99294262152782007-10-24IN100/300500722.660433696MALEMDexec-managerialcampinghusband5030002015-01-23Multi-vehicle CollisionFront CollisionMajor DamageFireOHSpringfield1705 Weaver St63YES12YES367003670734025690NissanPathfinder2010NNaN
993124286745702001-12-08OH250/50010001235.140443567MALEMDexec-managerialcampinghusband0-321002015-02-17Multi-vehicle CollisionSide CollisionTotal LossOtherOHHillsdale1643 Washington Hwy203?01?602006020602048160VolkswagenPassat2012NNaN
994141306814862007-03-24IN500/100010001347.040430665MALEHigh Schoolsalesbungie-jumpingown-child0-821002015-01-22Parked Car?Minor DamageNoneSCNorthbend6516 Solo Drive61?12YES648054010804860HondaCivic1996NNaN
9953389418511991-07-16OH500/100010001310.800431289FEMALEMasterscraft-repairpaintballunmarried002015-02-22Single Vehicle CollisionFront CollisionMinor DamageFireNCNorthbrook6045 Andromedia St201YES01?8720017440872061040HondaAccord2006NNaN
996285411869342014-01-05IL100/30010001436.790608177FEMALEPhDprof-specialtysleepingwife7090002015-01-24Single Vehicle CollisionRear CollisionMajor DamageFireSCNorthbend3092 Texas Drive231YES23?108480180801808072320VolkswagenPassat2015NNaN
997130349185162003-02-17OH250/5005001383.493000000442797FEMALEMastersarmed-forcesbungie-jumpingother-relative3510002015-01-23Multi-vehicle CollisionSide CollisionMinor DamagePoliceNCArlington7629 5th St43?23YES675007500750052500SuburuImpreza1996NNaN
998458625339402011-11-18IL500/100020001356.925000000441714MALEAssociatehandlers-cleanersbase-jumpingwife002015-02-26Single Vehicle CollisionRear CollisionMajor DamageOtherNYArlington6128 Elm Lane21?01YES469805220522036540AudiA51998NNaN
999456605560801996-11-11OH250/5001000766.190612260FEMALEAssociatesaleskayakinghusband002015-02-26Parked Car?Minor DamagePoliceWVColumbus1416 Cherokee Ridge61?03?50604609203680MercedesE4002007NNaN